LlamaIndex इंटीग्रेशन

# TypeScript / JavaScript
npm install seizn llamaindex

# Python
pip install seizn llama-index

import { SeiznRetriever } from 'seizn/llamaindex';
import { OpenAI } from 'llamaindex';
import { VectorStoreIndex, RetrieverQueryEngine } from 'llamaindex';

// Initialize the Seizn retriever
const retriever = new SeiznRetriever({
  apiKey: process.env.SEIZN_API_KEY,
  dataset: 'my-docs',
  topK: 5,
  threshold: 0.7,
});

// Create a query engine with the retriever
const llm = new OpenAI({ model: 'gpt-4' });
const queryEngine = new RetrieverQueryEngine(retriever, llm);

// Query your documents
const response = await queryEngine.query(
  'How do I configure rate limiting?'
);

console.log(response.response);
// Access the trace for debugging
console.log('Trace:', response.metadata?.seiznTrace);

import os
from seizn.llamaindex import SeiznRetriever
from llama_index.llms.openai import OpenAI
from llama_index.core.query_engine import RetrieverQueryEngine

# Initialize the Seizn retriever
retriever = SeiznRetriever(
    api_key=os.environ["SEIZN_API_KEY"],
    dataset="my-docs",
    top_k=5,
    threshold=0.7,
)

# Create a query engine with the retriever
llm = OpenAI(model="gpt-4")
query_engine = RetrieverQueryEngine.from_args(
    retriever=retriever,
    llm=llm,
)

# Query your documents
response = query_engine.query(
    "How do I configure rate limiting?"
)

print(response.response)
# Access the trace for debugging
print("Trace:", response.metadata.get("seizn_trace"))